Prospective evaluation of deep learning-based detection model for chest radiographs in outpatient respiratory clinic
Not Applicable
Active, not recruiting
- Conditions
- Diseases of the respiratory system
- Registration Number
- KCT0005466
- Lead Sponsor
- Konyang University Hospital
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Active, not recruiting
- Sex
- All
- Target Recruitment
- 329
Inclusion Criteria
Gender: both
Age: min. 20 years old
max. no limit
Adult who visit the outpatient clinic of department of Pulmonology to undergo chest X-ray
Exclusion Criteria
1. Those whose diagnostic model cannot be used due to poor quality of images.
2. Those who did not agree to participate in the study.
3. Those who are pregnant.
Study & Design
- Study Type
- Observational Study
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Diagnostic yield of radiologists and physicians for referable chest abnormality;Diagnostic performance comparisons between respiratory medicine physicians in the presence or absence of the AI aid
- Secondary Outcome Measures
Name Time Method umber of chest CT scans performed or reserved to be performed in each arm;Proportion of the chest CT scans with referable abnormalities;Outpatient clinic follow-up rate or recall rate;Medical source caused by the chest radiograph taken in respiratory medicine;False referral rate of radiologists and physicians for referable chest abnormality;Diagnostic performance of the AI algorithm for referable abnormalities in chest radiographs